回应顶会论文评审实验，探讨作者自评在机器学习评审中的应用与改进。
Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
- 将同行评审视为统计估计问题，用数学方法建模评审过程。
- 提出改进机制，减少评审中的不公平与策略性行为。
- 探索生成式AI时代下以人为本的评审新框架。
本文是对即将发表于《美国统计协会期刊》的《ICML 2023排名实验：考察机器学习/人工智能同行评审中的作者自评》一文的回应。针对讨论者提出的实践与理论问题，我们围绕四个核心主题展开回应：(i) 将同行评审建模为统计估计问题；(ii) 缓解隔离机制部署中的公平性与策略性问题；(iii) 融合评审人评分与结构化元数据等补充信号；(iv) 探索生成式AI时代的人本评审框架。
原文摘要 · Abstract (English)
This article is the rejoinder to ``The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review,'' to appear in the Journal of the American Statistical Association with discussion. To address the practical and theoretical points raised by the discussants, we organize our response around four core themes: (i) formulating peer review as a statistical estimation problem; (ii) mitigating equity and strategic concerns in the deployment of the Isotonic Mechanism; (iii) incorporating complementary signals such as reviewer rankings and structured metadata; and (iv) exploring a human-centered framework for peer review in the era of generative AI.
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